Co-Design and Refinement of Curriculum-Based Foodbot Factory Intervention to Support Elementary School Nutrition Education
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: School-based nutrition education interventions can support the development of children's food literacy and healthy eating habits. The Foodbot Factory serious game was developed to support school nutrition education based on Canada's Food Guide and Ontario curriculum. The objective of this research was to refine the Foodbot Factory intervention to include curriculum-based lesson plans that had a high-level of acceptability by stakeholders to support implementation by teachers in classrooms. METHODS: A co-design approach was used to engage teacher and dietitian stakeholders in developing five lesson plans for the intervention, who contributed to creating the intervention content in three stages. The stages included reviewing and providing feedback on the initial draft of the lesson plans, participating in facilitated discussion rounds to come to a consensus on the changes required, and completing a final review of the intervention's acceptability. Qualitative data included notes on the lesson plans and recordings from meetings that were analyzed thematically. RESULTS: During the first co-design stage, major revisions were suggested for two-fifths of the lessons by stakeholders. Further stakeholder suggestions were discussed and integrated into the intervention from facilitated discussions, improving the lesson plan content and intervention feasibility. All stakeholders agreed that the final version of the intervention was acceptable and would support classroom nutrition education. Five lesson plans were created and compiled into a unit plan, containing additional teaching resources, to support nutrition education with Foodbot Factory. CONCLUSIONS: The co-design process greatly improved the Foodbot Factory intervention and its feasibility for classroom implementation. Including diverse stakeholder perspectives led to unique and different insights to improve the intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".